Comparison of Application of AERMOD and ISCST3 Models for Simulating the Dispersion of Emitted Pollutant from the Stack of an Industrial Plant in Different Time Scales

Abstract

Background: One of the main parts of air quality management is known as modeling of atmospheric pollutants. In this regards, simultaneous application of several models in a project and comparing the results obtained from these models could have been a considerable contribution to air quality managers for taking a more efficient decision. Methods: In this study, the stack of an industrial plant in the southwest of Isfahan was selected as the emission source and the total suspended particles emitted from this stack was simulated by applying AERMOD and ISCST3 view models (version 8.2). In this vein, the modeling process was conducted using MM5 meteorological data in a 50 50 km extent with 2000 m network distance for each of the models in 1-h, 24-h term averages (short term averages) and monthly and annual periods (long term averages) at ground level concentrations (GLC). Results: Results indicated that the highest simulated concentration for both models occurred in a 2000 meters’ distance in the east of the stack. Moreover, the highest simulated concentration applying AERMOD was lower than that of applying ISCST3 in all term averages which is due to existing differences between applied algorithms in these two models. Conclusion: Consequently, applying AERMOD due to the use of more advanced and up-to-date algorithms have priority over ISCST3 model. Applying ISCST3 can also be useful for small projects that require less input data compared to the AERMOD.

Assessment of PM2.5 Chemical Composition and Air Quality Monitoring: Implications of Air Pollutants Emissions from a Night Market in Kaohsiung City, Taiwan

“…It can assess pollution levels released by various sources in industrial parks. ISCST3 has been proved in numerous studies to be a better model for simulating the dispersion of air contaminants, notably from industrial sources (Bajoghli, 2019;Karuna et al, 2017;Prakash et al, 2017).…”

“…They discovered that ISCST3 was better at forecasting CO concentrations, while ADMS-Urban was much better at predicting NO2 concentrations, and all three models were accurate at predicting CO and PM2.5 concentrations. Different modeling methods and/or landscape feature processing could cause variances in model performance Bajoghli (2019). explored AERMOD and ISCST3 models for the simulations of pollutant dispersion from factory chimneys.…”